plotTopology: Plot a topological feature of network module
In InouyeLab/NetRep: Permutation Testing Network Module Preservation Across Datasets
Description
Usage
Arguments
Details
See Also
Examples
Description
Functions for plotting the topology of a network module.
Usage
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plotData(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderSamplesBy = NULL,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotSampleNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Data",
legend.main.line = 1,
naxt.line = -0.5,
saxt.line = -0.5,
maxt.line = 3,
legend.position = 0.15,
laxt.tck = 0.03,
laxt.line = 3,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
dataCols = NULL,
dataRange = NULL,
naCol = "#bdbdbd",
dryRun = FALSE
)
plotCorrelation(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
symmetric = FALSE,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Correlation",
legend.main.line = 1,
naxt.line = -0.5,
maxt.line = 3,
legend.position = NULL,
laxt.tck = NULL,
laxt.line = NULL,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
corCols = correlation.palette(),
corRange = c(-1, 1),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotNetwork(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
symmetric = FALSE,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Edge weight",
legend.main.line = 1,
naxt.line = -0.5,
maxt.line = 3,
legend.position = NULL,
laxt.tck = NULL,
laxt.line = NULL,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
netCols = network.palette(),
netRange = c(0, 1),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotContribution(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
ylab.line = 2.5,
lwd = 1,
drawBorders = FALSE,
naxt.line = -0.5,
maxt.line = 3,
yaxt.line = 0,
yaxt.tck = -0.035,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
contribCols = c("#A50026", "#313695"),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotDegree(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
drawBorders = FALSE,
naxt.line = -0.5,
maxt.line = 3,
yaxt.line = 0,
yaxt.tck = -0.035,
ylab.line = 2.5,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
degreeCol = "#feb24c",
naCol = "#bdbdbd",
dryRun = FALSE
)
plotSummary(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderSamplesBy = NULL,
orderNodesBy = NULL,
orderModules = TRUE,
plotSampleNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
xlab.line = 2.5,
lwd = 1,
drawBorders = FALSE,
saxt.line = -0.5,
maxt.line = 0,
xaxt.line = 0,
xaxt.tck = -0.025,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
summaryCols = c("#1B7837", "#762A83"),
naCol = "#bdbdbd",
dryRun = FALSE
)
Arguments
network
a list of interaction networks, one for each dataset. Each
entry of the list should be a n * n matrix or where each element
contains the edge weight between nodes i and j in the inferred
network for that dataset.
data
a list of matrices, one for each dataset. Each entry of the list
should be the data used to infer the interaction network for that
dataset. The columns should correspond to variables in the data
(nodes in the network) and rows to samples in that dataset.
correlation
a list of matrices, one for each dataset. Each entry of
the list should be a n * n matrix where each element contains the
correlation coefficient between nodes i and j in the
data used to infer the interaction network for that dataset.
moduleAssignments
a list of vectors, one for each discovery
dataset, containing the module assignments for each node in that dataset.
modules
a list of vectors, one for each discovery dataset,
of modules to perform the analysis on. If unspecified, all modules
in each discovery dataset will be analysed, with the exception of
those specified in backgroundLabel argument.
backgroundLabel
a single label given to nodes that do not belong to
any module in the moduleAssignments argument. Defaults to "0". Set
to NULL if you do not want to skip the network background module.
discovery
a vector of names or indices denoting the discovery
dataset(s) in the data, correlation, network,
moduleAssignments, modules, and test lists.
test
a list of vectors, one for each discovery dataset,
of names or indices denoting the test dataset(s) in the data,
correlation, and network lists.
verbose
logical; should progress be reported? Default is TRUE.
orderSamplesBy
NULL (default), NA, or a vector
containing a single dataset name or index. Controls how samples are ordered
on the plot (see details).
orderNodesBy
NULL (default), NA, or a vector of dataset
names or indices. Controls how nodes are ordered on the plot (see details).
orderModules
logical; if TRUE modules ordered by clustering
their summary vectors. If FALSE modules are returned in the order
provided.
plotNodeNames
logical; controls whether the node names are
drawed on the bottom axis.
plotSampleNames
logical; controls whether the sample names are
drawed on the left axis.
plotModuleNames
logical; controls whether module names are drawed.
The default is for module names to be drawed when multiple modules
are drawn.
main
title for the plot.
main.line
the number of lines into the top margin at which the plot
title will be drawn.
lwd
line width for borders and axes.
plotLegend
logical; controls whether a legend is drawn when using
plotCorrelation, plotNetwork, or plotData.
legend.main
title for the legend.
legend.main.line
the distance from the legend to draw the legend
title.
naxt.line
the number of lines into the bottom margin at which the node
names will be drawn.
saxt.line
the number of lines into the left margin at which the sample
names will be drawn.
maxt.line
the number of lines into the bottom margin at which the
module names will be drawn.
legend.position
the distance from the plot to start the legend, as a
proportion of the plot width.
laxt.tck
size of the ticks on each axis legend relative to the
size of the correlation, edge weights, and data matrix heatmaps.
laxt.line
the distance from the legend to draw the legend axis
labels, as multiple of laxt.tck.
cex.axis
relative size of the node and sample names.
cex.lab
relative size of the module names and legend titles.
cex.main
relative size of the plot titles.
dataCols
a character vector of colors to create a gradient from for
the data heatmap (see details). Automatically determined if NA or
NULL.
dataRange
the range of values to map to the dataCols gradient
(see details). Automatically determined if NA or NULL.
naCol
color to use for missing nodes and samples on the data,
correlation structure, and network edge weight heat maps.
dryRun
logical; if TRUE, only the axes and labels will be
drawed.
symmetric
logical; controls whether the correlation and network
heatmaps are drawn as symmetric (square) heatmaps or asymettric triangle
heatmaps. If symmetric, then the node and module names will also be rendered
on the left axis.
corCols
a character vector of colors to create a gradient from for
the correlation structure heatmap (see details).
corRange
the range of values to map to the corCols gradient
(see details).
netCols
a character vector of colors to create a gradient from for
the network edge weight heatmap (see details).
netRange
the range of values to map to the corCols gradient
(see details). Automatically determined if NA or NULL.
ylab.line
the number of lines into the left margin at which the
y axis labels on the weighted degree and node contribution
bar plots will be drawn.
drawBorders
logical; if TRUE, borders are drawn around the
weighted degree, node conribution, and module summary
bar plots.
yaxt.line
the number of lines into the left margin at which the
y-axis tick labels will be drawn on the weighted degree and node
contribution bar plots.
yaxt.tck
the size of the y-axis ticks for the weighted degree and
node contribution bar plots.
contribCols
color(s) to use for the node contribution bar plot
(see details).
degreeCol
color to use for the weighted degree bar plot.
xlab.line
the number of lines into the bottom margin at which the
x axis label on the module summary bar plot(s) will be drawn.
xaxt.line
the number of lines into the bottom margin at which the
x-axis tick labels will be drawn on the module summary bar plot.
xaxt.tck
the size of the x-axis ticks for the module summary bar
plot.
summaryCols
color(s) to use for the node contribution bar plot
(see details).
Details
Input data structures:
The preservation of network modules in a second
dataset is quantified by measuring the preservation of topological
properties between the discovery and test datasets. These
properties are calculated not only from the interaction networks inferred
in each dataset, but also from the data used to infer those networks (e.g.
gene expression data) as well as the correlation structure between
variables/nodes. Thus, all functions in the NetRep package have the
following arguments:
network:
a list of interaction networks, one for each dataset.
data:
a list of data matrices used to infer those networks, one for each
dataset.
correlation:
a list of matrices containing the pairwise correlation coefficients
between variables/nodes in each dataset.
moduleAssignments:
a list of vectors, one for each discovery dataset, containing
the module assignments for each node in that dataset.
modules:
a list of vectors, one for each discovery dataset, containing
the names of the modules from that dataset to analyse.
discovery:
a vector indicating the names or indices of the previous arguments'
lists to use as the discovery dataset(s) for the analyses.
test:
a list of vectors, one vector for each discovery dataset,
containing the names or indices of the network, data, and
correlation argument lists to use as the test dataset(s)
for the analysis of each discovery dataset.
The formatting of these arguments is not strict: each function will attempt
to make sense of the user input. For example, if there is only one
discovery dataset, then input to the moduleAssigments and
test arguments may be vectors, rather than lists. If the node and
sample ordering is being calculated within the same dataset being
visualised, then the discovery and test arguments do
not need to be specified, and the input matrices for the network,
data, and correlation arguments do not need to be wrapped in
a list.
Analysing large datasets:
Matrices in the network, data, and correlation lists
can be supplied as disk.matrix objects. This class allows
matrix data to be kept on disk and loaded as required by NetRep.
This dramatically decreases memory usage: the matrices for only one
dataset will be kept in RAM at any point in time.
Node, sample, and module ordering:
By default, nodes are ordered in decreasing order of weighted degree
in the discovery dataset (see nodeOrder). Missing
nodes are colored in grey. This facilitates the visual comparison of
modules across datasets, as the node ordering will be preserved.
Alternatively, a vector containing the names or indices of one or more
datasets can be provided to the orderNodesBy argument.
If a single dataset is provided, then nodes will be ordered in decreasing
order of weighted degree in that dataset. Only nodes that are
present in this dataset will be drawn when ordering nodes by a dataset
that is not the discovery dataset for the requested modules(s).
If multiple datasets are provided then the weighted degree will be
averaged across these datasets (see nodeOrder for more details).
This is useful for obtaining a robust ordering of nodes by relative
importance, assuming the modules displayed are preserved in those
datasets.
Ordering of nodes by weighted degree can be suppressed by setting
orderNodesBy to NA, in which case nodes will be ordered as
in the matrices provided in the network, data, and
correlation arguments.
When multiple modules are drawn, modules are ordered by the similarity
of their summary vectors in the dataset(s) specified in orderNodesBy
argument. If multiple datasets are provided to the orderNodesBy
argument then the module summary vectors are concatenated across datasets.
By default, samples in the data heatmap and accompanying module summary bar
plot are ordered in descending order of module summary in the drawn
dataset (specified by the test argument). If multiple modules are
drawn, samples are ordered as per the left-most module on the plot.
Alternatively, a vector containing the name or index of another dataset
may be provided to the orderSamplesBy argument. In this case,
samples will be ordered in descending order of module summary in
the specified dataset. This is useful when comparing different
measurements across samples, for example, gene expression data obtained
from multiple tissues samples across the same individuals. If the dataset
specified is the discovery dataset, then missing samples will be
displayed as horizontal grey bars. If the dataset specified is one of the
other datasets, samples present in both the specified dataset and the
test dataset will be displayed first in order of the specified
dataset, then samples present in only the test dataset will be displayed
underneath a horizontal black line ordered by their module summary vector
in the test dataset.
Order of samples by module summary can be suppressed by setting
orderSamplesBy to NA, in which case samples will be order as
in the matrix provided to the data argument for the drawn dataset.
Weighted degree scaling:
When drawn on a plot, the weighted degree of each node is scaled to the
maximum weighted degree within its module. The scaled weighted degree is
measure of relative importance for each node to its module. This makes
visualisation of multiple modules with different sizes and densities
possible. However, the scaled weighted degree should only be interpreted
for groups of nodes that have an apparent module structure.
Plot layout and device size
Although reasonable default values for most parameters have been provided,
the rendering of axes and titles may need adjusting depending on the size
of the plot window. The dryRun argument is useful for quickly
determining whether the plot will render correctly. When dryRun
is TRUE only the axes, legends, labels, and title will be drawn,
allowing for quick iteration of customisable parameters to get the plot
layout correct.
Warning: PDF and other vectorized devices should not be used when
plotting the heatmaps with more than a hundred nodes. Large files will be
generated which may cause image editing programs such as Inkscape or
Illustrator to crash when polishing figures for publication.
If axis labels or legends are drawn off screen then the margins of the
plot should be adjusted prior to plotting using the
par command to increase the margin size
(see the "mar" option in the par help page).
The size of text labels can be modified by increasing or decreasing the
cex.main, cex.lab, and cex.axis arguments:
cex.main: controls the size of the plot title (specified
in the main argument).
cex.lab: controls the size of the axis labels on the
weighted degree, node contribution,
and module summary bar plots as well as
the size of the module labels and the heatmap
legend titles.
cex.axis: contols the size of the axis tick labels,
including the node and sample labels.
The position of these labels can be changed through the following
arguments:
xaxt.line: controls the distance from the plot the x-axis
tick labels are drawn on the module summary bar plot.
xlab.line: controls the distance from the plot the x-axis
label is drawn on the module summary bar plot.
yaxt.line: controls the distance from the plot the y-axis
tick labels are drawn on the weighted degree and
node contribution bar plots.
ylab.line: controls the distance from the plot the y-axis
label is drawn on the weighted degree and
node contribution bar plots.
main.line: controls the distance from the plot the title is
drawn.
naxt.line: controls the distance from the plot the node
labels are drawn.
saxt.line: controls the distance from the plot the sample
labels are drawn.
maxt.line: controls the distance from the plot the module
labels are drawn.
laxt.line: controls the distance from the heatmap legends
that the gradient legend labels are drawn.
legend.main.line: controls the distance from the heatmap
legends that the legend title is drawn.
The rendering of node, sample, and module names can be disabled by setting
plotNodeNames, plotSampleNames, and plotModuleNames
to FALSE.
The size of the axis ticks can be changed by increasing or decreasing the
following arguments:
xaxt.tck: size of the x-axis tick labels as a multiple of
the height of the module summary bar plot
yaxt.tck: size of the y-axis tick labels as a multiple of
the width of the weighted degree or node contribution
bar plots.
laxt.tck: size of the heatmap legend axis ticks as a
multiple of the width of the data, correlation structure, or
network edge weight heatmaps.
The placement of heatmap legends is controlled by the following arguments:
plotLegend: if FALSE legend will not be drawn.
legend.position: a multiple of the plot width, controls
the horizontal distance from the plot the legend is drawn.
The drawBorders argument controls whether borders are drawn around
the weighted degree, node contribution, or module summary bar plots. The
lwd argument controls the thickness of these borders, as well as
the thickness of axes and axis ticks.
Modifying the color palettes:
The dataCols and dataRange arguments control the appearance
of the data heatmap (see plotData). The gradient of colors
used on the heatmap can be changed by specifying a vector of colors to
interpolate between in dataCols and dataRange specifies the
range of values that maps to this gradient. Values outside of the
specified dataRange will be rendered with the colors used at either
extreme of the gradient. The default gradient is determined based on the
data shown on the plot. If all values in the data matrix are
positive, then the gradient is interpolated between white and green, where
white is used for the smallest value and green for the largest. If all
values are negative, then the gradient is interpolated between purple and
white, where purple is used for the smallest value and white for the value
closest to zero. If the data contains both positive and negative values,
then the gradient is interpolated between purple, white, and green, where
white is used for values of zero. In this case the range shown is always
centered at zero, with the values at either extreme determined by the
value in the rendered data with the strongest magnitude (the
maximum of the absolute value).
The corCols and corRange arguments control the appearance of
the correlation structure heatmap (see plotCorrelation). The
gradient of colors used on the heatmap can be changed by specifying a
vector of colors to interpolate between in corCols. By default,
strong negative correlations are shown in blue, and strong positive
correlations in red, and weak correlations as white. corRange
controls the range of values that this gradient maps to, by default, -1 to
1. Changing this may be useful for showing differences where range of
correlation coefficients is small.
The netCols and netRange arguments control the appearance of
the network edge weight heatmap (see plotNetwork). The
gradient of colors used on the heatmap can be changed by specifying a
vector of colors to interpolate between in netCols. By default,
weak or non-edges are shown in white, while strong edges are shown in red.
The netRange controls the range of values this gradient maps to,
by default, 0 to 1. If netRange is set to NA, then the
gradient will be mapped to values between 0 and the maximum edge weight of
the shown network.
The degreeCol argument controls the color of the weighted degree
bar plot (see plotDegree).
The contribCols argument controls the color of the node
contribution bar plot (see plotContribution. This can be
specified as single value to be used for all nodes, or as two colors: one
to use for nodes with positive contributions and one to use for nodes with
negative contributions.
The summaryCols argument controls the color of the module summary
bar plot (see plotSummary. This can be specified as single
value to be used for all samples, or as two colors: one to use for samples
with a positive module summary value and one fpr samples with a negative
module summary value.
The naCol argument controls the color of missing nodes and samples
on the data, correlaton structure, and network edge weight heatmaps.
See Also
plotModule for a combined plot showing all topological
properties for a network module.
Examples
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# load in example data, correlation, and network matrices for a discovery and test dataset:
data("NetRep")
# Set up input lists for each input matrix type across datasets. The list
# elements can have any names, so long as they are consistent between the
# inputs.
network_list <- list(discovery=discovery_network, test=test_network)
data_list <- list(discovery=discovery_data, test=test_data)
correlation_list <- list(discovery=discovery_correlation, test=test_correlation)
labels_list <- list(discovery=module_labels)
# Plot the data for module 1, 2 and 4 in the discovery dataset
plotData(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4)
)
# Symmetric = TRUE gives a traditional heatmap for the correlation structure
# and weighted network
plotCorrelation(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), symmetric=TRUE
)
# While the default is to render only one half of the (symmetric) matrix
plotNetwork(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4)
)
# Plot the degree of nodes in each module in the test dataset, but show them
# in the same order as the discovery dataset to compare how node degree
# changes
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test"
)
# Alternatively nodes can be ordered on the plot by degree in the test dataset
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderNodesBy="test"
)
# Or by averaging the degree across datasets for a more robust ordering
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderNodesBy=c("discovery", "test")
)
# Arbitrary subsets can be plotted:
plotContribution(
network=network_list[[1]][1:10, 1:10], data=data_list[[1]][, 1:10],
correlation=correlation_list[[1]][1:10, 1:10], orderNodesBy=NA
)
# Plot the module summary vectors for multiple modules:
plotSummary(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderSamplesBy="test"
)
InouyeLab/NetRep documentation built on Oct. 8, 2020, 2:32 a.m.
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Description Usage Arguments Details See Also Examples
Description
Functions for plotting the topology of a network module.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | plotData(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderSamplesBy = NULL,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotSampleNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Data",
legend.main.line = 1,
naxt.line = -0.5,
saxt.line = -0.5,
maxt.line = 3,
legend.position = 0.15,
laxt.tck = 0.03,
laxt.line = 3,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
dataCols = NULL,
dataRange = NULL,
naCol = "#bdbdbd",
dryRun = FALSE
)
plotCorrelation(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
symmetric = FALSE,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Correlation",
legend.main.line = 1,
naxt.line = -0.5,
maxt.line = 3,
legend.position = NULL,
laxt.tck = NULL,
laxt.line = NULL,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
corCols = correlation.palette(),
corRange = c(-1, 1),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotNetwork(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
symmetric = FALSE,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
plotLegend = TRUE,
legend.main = "Edge weight",
legend.main.line = 1,
naxt.line = -0.5,
maxt.line = 3,
legend.position = NULL,
laxt.tck = NULL,
laxt.line = NULL,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
netCols = network.palette(),
netRange = c(0, 1),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotContribution(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
ylab.line = 2.5,
lwd = 1,
drawBorders = FALSE,
naxt.line = -0.5,
maxt.line = 3,
yaxt.line = 0,
yaxt.tck = -0.035,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
contribCols = c("#A50026", "#313695"),
naCol = "#bdbdbd",
dryRun = FALSE
)
plotDegree(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderNodesBy = NULL,
orderModules = TRUE,
plotNodeNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
lwd = 1,
drawBorders = FALSE,
naxt.line = -0.5,
maxt.line = 3,
yaxt.line = 0,
yaxt.tck = -0.035,
ylab.line = 2.5,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
degreeCol = "#feb24c",
naCol = "#bdbdbd",
dryRun = FALSE
)
plotSummary(
network,
data,
correlation,
moduleAssignments = NULL,
modules = NULL,
backgroundLabel = "0",
discovery = NULL,
test = NULL,
verbose = TRUE,
orderSamplesBy = NULL,
orderNodesBy = NULL,
orderModules = TRUE,
plotSampleNames = TRUE,
plotModuleNames = NULL,
main = "",
main.line = 1,
xlab.line = 2.5,
lwd = 1,
drawBorders = FALSE,
saxt.line = -0.5,
maxt.line = 0,
xaxt.line = 0,
xaxt.tck = -0.025,
cex.axis = 0.8,
cex.lab = 1.2,
cex.main = 2,
summaryCols = c("#1B7837", "#762A83"),
naCol = "#bdbdbd",
dryRun = FALSE
)
|
Arguments
network |
a list of interaction networks, one for each dataset. Each entry of the list should be a n * n matrix or where each element contains the edge weight between nodes i and j in the inferred network for that dataset. |
data |
a list of matrices, one for each dataset. Each entry of the list
should be the data used to infer the interaction |
correlation |
a list of matrices, one for each dataset. Each entry of
the list should be a n * n matrix where each element contains the
correlation coefficient between nodes i and j in the
|
moduleAssignments |
a list of vectors, one for each discovery dataset, containing the module assignments for each node in that dataset. |
modules |
a list of vectors, one for each |
backgroundLabel |
a single label given to nodes that do not belong to
any module in the |
discovery |
a vector of names or indices denoting the discovery
dataset(s) in the |
test |
a list of vectors, one for each |
verbose |
logical; should progress be reported? Default is |
orderSamplesBy |
|
orderNodesBy |
|
orderModules |
logical; if |
plotNodeNames |
logical; controls whether the node names are drawed on the bottom axis. |
plotSampleNames |
logical; controls whether the sample names are drawed on the left axis. |
plotModuleNames |
logical; controls whether module names are drawed.
The default is for module names to be drawed when multiple |
main |
title for the plot. |
main.line |
the number of lines into the top margin at which the plot title will be drawn. |
lwd |
line width for borders and axes. |
plotLegend |
logical; controls whether a legend is drawn when using
|
legend.main |
title for the legend. |
legend.main.line |
the distance from the legend to draw the legend title. |
naxt.line |
the number of lines into the bottom margin at which the node names will be drawn. |
saxt.line |
the number of lines into the left margin at which the sample names will be drawn. |
maxt.line |
the number of lines into the bottom margin at which the module names will be drawn. |
legend.position |
the distance from the plot to start the legend, as a proportion of the plot width. |
laxt.tck |
size of the ticks on each axis legend relative to the size of the correlation, edge weights, and data matrix heatmaps. |
laxt.line |
the distance from the legend to draw the legend axis
labels, as multiple of |
cex.axis |
relative size of the node and sample names. |
cex.lab |
relative size of the module names and legend titles. |
cex.main |
relative size of the plot titles. |
dataCols |
a character vector of colors to create a gradient from for
the data heatmap (see details). Automatically determined if |
dataRange |
the range of values to map to the |
naCol |
color to use for missing nodes and samples on the data, correlation structure, and network edge weight heat maps. |
dryRun |
logical; if |
symmetric |
logical; controls whether the correlation and network heatmaps are drawn as symmetric (square) heatmaps or asymettric triangle heatmaps. If symmetric, then the node and module names will also be rendered on the left axis. |
corCols |
a character vector of colors to create a gradient from for the correlation structure heatmap (see details). |
corRange |
the range of values to map to the |
netCols |
a character vector of colors to create a gradient from for the network edge weight heatmap (see details). |
netRange |
the range of values to map to the |
ylab.line |
the number of lines into the left margin at which the y axis labels on the weighted degree and node contribution bar plots will be drawn. |
drawBorders |
logical; if |
yaxt.line |
the number of lines into the left margin at which the y-axis tick labels will be drawn on the weighted degree and node contribution bar plots. |
yaxt.tck |
the size of the y-axis ticks for the weighted degree and node contribution bar plots. |
contribCols |
color(s) to use for the node contribution bar plot (see details). |
degreeCol |
color to use for the weighted degree bar plot. |
xlab.line |
the number of lines into the bottom margin at which the x axis label on the module summary bar plot(s) will be drawn. |
xaxt.line |
the number of lines into the bottom margin at which the x-axis tick labels will be drawn on the module summary bar plot. |
xaxt.tck |
the size of the x-axis ticks for the module summary bar plot. |
summaryCols |
color(s) to use for the node contribution bar plot (see details). |
Details
Input data structures:
The preservation of network modules in a second
dataset is quantified by measuring the preservation of topological
properties between the discovery and test datasets. These
properties are calculated not only from the interaction networks inferred
in each dataset, but also from the data used to infer those networks (e.g.
gene expression data) as well as the correlation structure between
variables/nodes. Thus, all functions in the NetRep package have the
following arguments:
network: a list of interaction networks, one for each dataset.data: a list of data matrices used to infer those networks, one for each dataset.correlation: a list of matrices containing the pairwise correlation coefficients between variables/nodes in each dataset.moduleAssignments: a list of vectors, one for each discovery dataset, containing the module assignments for each node in that dataset.modules: a list of vectors, one for each discovery dataset, containing the names of the modules from that dataset to analyse.discovery: a vector indicating the names or indices of the previous arguments' lists to use as the discovery dataset(s) for the analyses.test: a list of vectors, one vector for each discovery dataset, containing the names or indices of thenetwork,data, andcorrelationargument lists to use as the test dataset(s) for the analysis of each discovery dataset.
The formatting of these arguments is not strict: each function will attempt
to make sense of the user input. For example, if there is only one
discovery dataset, then input to the moduleAssigments and
test arguments may be vectors, rather than lists. If the node and
sample ordering is being calculated within the same dataset being
visualised, then the discovery and test arguments do
not need to be specified, and the input matrices for the network,
data, and correlation arguments do not need to be wrapped in
a list.
Analysing large datasets:
Matrices in the network, data, and correlation lists
can be supplied as disk.matrix objects. This class allows
matrix data to be kept on disk and loaded as required by NetRep.
This dramatically decreases memory usage: the matrices for only one
dataset will be kept in RAM at any point in time.
Node, sample, and module ordering:
By default, nodes are ordered in decreasing order of weighted degree
in the discovery dataset (see nodeOrder). Missing
nodes are colored in grey. This facilitates the visual comparison of
modules across datasets, as the node ordering will be preserved.
Alternatively, a vector containing the names or indices of one or more
datasets can be provided to the orderNodesBy argument.
If a single dataset is provided, then nodes will be ordered in decreasing
order of weighted degree in that dataset. Only nodes that are
present in this dataset will be drawn when ordering nodes by a dataset
that is not the discovery dataset for the requested modules(s).
If multiple datasets are provided then the weighted degree will be averaged across these datasets (see nodeOrder for more details). This is useful for obtaining a robust ordering of nodes by relative importance, assuming the modules displayed are preserved in those datasets.
Ordering of nodes by weighted degree can be suppressed by setting
orderNodesBy to NA, in which case nodes will be ordered as
in the matrices provided in the network, data, and
correlation arguments.
When multiple modules are drawn, modules are ordered by the similarity
of their summary vectors in the dataset(s) specified in orderNodesBy
argument. If multiple datasets are provided to the orderNodesBy
argument then the module summary vectors are concatenated across datasets.
By default, samples in the data heatmap and accompanying module summary bar
plot are ordered in descending order of module summary in the drawn
dataset (specified by the test argument). If multiple modules are
drawn, samples are ordered as per the left-most module on the plot.
Alternatively, a vector containing the name or index of another dataset
may be provided to the orderSamplesBy argument. In this case,
samples will be ordered in descending order of module summary in
the specified dataset. This is useful when comparing different
measurements across samples, for example, gene expression data obtained
from multiple tissues samples across the same individuals. If the dataset
specified is the discovery dataset, then missing samples will be
displayed as horizontal grey bars. If the dataset specified is one of the
other datasets, samples present in both the specified dataset and the
test dataset will be displayed first in order of the specified
dataset, then samples present in only the test dataset will be displayed
underneath a horizontal black line ordered by their module summary vector
in the test dataset.
Order of samples by module summary can be suppressed by setting
orderSamplesBy to NA, in which case samples will be order as
in the matrix provided to the data argument for the drawn dataset.
Weighted degree scaling:
When drawn on a plot, the weighted degree of each node is scaled to the maximum weighted degree within its module. The scaled weighted degree is measure of relative importance for each node to its module. This makes visualisation of multiple modules with different sizes and densities possible. However, the scaled weighted degree should only be interpreted for groups of nodes that have an apparent module structure.
Plot layout and device size
Although reasonable default values for most parameters have been provided,
the rendering of axes and titles may need adjusting depending on the size
of the plot window. The dryRun argument is useful for quickly
determining whether the plot will render correctly. When dryRun
is TRUE only the axes, legends, labels, and title will be drawn,
allowing for quick iteration of customisable parameters to get the plot
layout correct.
Warning: PDF and other vectorized devices should not be used when plotting the heatmaps with more than a hundred nodes. Large files will be generated which may cause image editing programs such as Inkscape or Illustrator to crash when polishing figures for publication.
If axis labels or legends are drawn off screen then the margins of the
plot should be adjusted prior to plotting using the
par command to increase the margin size
(see the "mar" option in the par help page).
The size of text labels can be modified by increasing or decreasing the
cex.main, cex.lab, and cex.axis arguments:
cex.main: controls the size of the plot title (specified in themainargument).cex.lab: controls the size of the axis labels on the weighted degree, node contribution, and module summary bar plots as well as the size of the module labels and the heatmap legend titles.cex.axis: contols the size of the axis tick labels, including the node and sample labels.
The position of these labels can be changed through the following arguments:
xaxt.line: controls the distance from the plot the x-axis tick labels are drawn on the module summary bar plot.xlab.line: controls the distance from the plot the x-axis label is drawn on the module summary bar plot.yaxt.line: controls the distance from the plot the y-axis tick labels are drawn on the weighted degree and node contribution bar plots.ylab.line: controls the distance from the plot the y-axis label is drawn on the weighted degree and node contribution bar plots.main.line: controls the distance from the plot the title is drawn.naxt.line: controls the distance from the plot the node labels are drawn.saxt.line: controls the distance from the plot the sample labels are drawn.maxt.line: controls the distance from the plot the module labels are drawn.laxt.line: controls the distance from the heatmap legends that the gradient legend labels are drawn.legend.main.line: controls the distance from the heatmap legends that the legend title is drawn.
The rendering of node, sample, and module names can be disabled by setting
plotNodeNames, plotSampleNames, and plotModuleNames
to FALSE.
The size of the axis ticks can be changed by increasing or decreasing the following arguments:
xaxt.tck: size of the x-axis tick labels as a multiple of the height of the module summary bar plotyaxt.tck: size of the y-axis tick labels as a multiple of the width of the weighted degree or node contribution bar plots.laxt.tck: size of the heatmap legend axis ticks as a multiple of the width of the data, correlation structure, or network edge weight heatmaps.
The placement of heatmap legends is controlled by the following arguments:
plotLegend: ifFALSElegend will not be drawn.legend.position: a multiple of the plot width, controls the horizontal distance from the plot the legend is drawn.
The drawBorders argument controls whether borders are drawn around
the weighted degree, node contribution, or module summary bar plots. The
lwd argument controls the thickness of these borders, as well as
the thickness of axes and axis ticks.
Modifying the color palettes:
The dataCols and dataRange arguments control the appearance
of the data heatmap (see plotData). The gradient of colors
used on the heatmap can be changed by specifying a vector of colors to
interpolate between in dataCols and dataRange specifies the
range of values that maps to this gradient. Values outside of the
specified dataRange will be rendered with the colors used at either
extreme of the gradient. The default gradient is determined based on the
data shown on the plot. If all values in the data matrix are
positive, then the gradient is interpolated between white and green, where
white is used for the smallest value and green for the largest. If all
values are negative, then the gradient is interpolated between purple and
white, where purple is used for the smallest value and white for the value
closest to zero. If the data contains both positive and negative values,
then the gradient is interpolated between purple, white, and green, where
white is used for values of zero. In this case the range shown is always
centered at zero, with the values at either extreme determined by the
value in the rendered data with the strongest magnitude (the
maximum of the absolute value).
The corCols and corRange arguments control the appearance of
the correlation structure heatmap (see plotCorrelation). The
gradient of colors used on the heatmap can be changed by specifying a
vector of colors to interpolate between in corCols. By default,
strong negative correlations are shown in blue, and strong positive
correlations in red, and weak correlations as white. corRange
controls the range of values that this gradient maps to, by default, -1 to
1. Changing this may be useful for showing differences where range of
correlation coefficients is small.
The netCols and netRange arguments control the appearance of
the network edge weight heatmap (see plotNetwork). The
gradient of colors used on the heatmap can be changed by specifying a
vector of colors to interpolate between in netCols. By default,
weak or non-edges are shown in white, while strong edges are shown in red.
The netRange controls the range of values this gradient maps to,
by default, 0 to 1. If netRange is set to NA, then the
gradient will be mapped to values between 0 and the maximum edge weight of
the shown network.
The degreeCol argument controls the color of the weighted degree
bar plot (see plotDegree).
The contribCols argument controls the color of the node
contribution bar plot (see plotContribution. This can be
specified as single value to be used for all nodes, or as two colors: one
to use for nodes with positive contributions and one to use for nodes with
negative contributions.
The summaryCols argument controls the color of the module summary
bar plot (see plotSummary. This can be specified as single
value to be used for all samples, or as two colors: one to use for samples
with a positive module summary value and one fpr samples with a negative
module summary value.
The naCol argument controls the color of missing nodes and samples
on the data, correlaton structure, and network edge weight heatmaps.
See Also
plotModule for a combined plot showing all topological
properties for a network module.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # load in example data, correlation, and network matrices for a discovery and test dataset:
data("NetRep")
# Set up input lists for each input matrix type across datasets. The list
# elements can have any names, so long as they are consistent between the
# inputs.
network_list <- list(discovery=discovery_network, test=test_network)
data_list <- list(discovery=discovery_data, test=test_data)
correlation_list <- list(discovery=discovery_correlation, test=test_correlation)
labels_list <- list(discovery=module_labels)
# Plot the data for module 1, 2 and 4 in the discovery dataset
plotData(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4)
)
# Symmetric = TRUE gives a traditional heatmap for the correlation structure
# and weighted network
plotCorrelation(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), symmetric=TRUE
)
# While the default is to render only one half of the (symmetric) matrix
plotNetwork(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4)
)
# Plot the degree of nodes in each module in the test dataset, but show them
# in the same order as the discovery dataset to compare how node degree
# changes
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test"
)
# Alternatively nodes can be ordered on the plot by degree in the test dataset
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderNodesBy="test"
)
# Or by averaging the degree across datasets for a more robust ordering
plotDegree(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderNodesBy=c("discovery", "test")
)
# Arbitrary subsets can be plotted:
plotContribution(
network=network_list[[1]][1:10, 1:10], data=data_list[[1]][, 1:10],
correlation=correlation_list[[1]][1:10, 1:10], orderNodesBy=NA
)
# Plot the module summary vectors for multiple modules:
plotSummary(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
test="test", orderSamplesBy="test"
)
|
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